用AI自动分类用户邮件主题，提升客服效率
Improving Customer Service with Automatic Topic Detection in User Emails
- 基于BERTopic构建无监督话题建模流程，自动识别邮件主题
- 处理速度仅0.041秒/封，准确率F1达0.96，12个主话题精准标注
- 适用于低资源、形态丰富的语言，可推广至多语种客服系统
本研究提出一种新型自然语言处理流水线，用于提升塞尔维亚电信公司Telekom Srbija的客户服务效率。通过自动化邮件主题检测与标注，核心采用BERTopic这一模块化框架实现无监督话题建模。经过一系列预处理和后处理步骤，系统为每封邮件分配12个主话题及若干附加标签，并通过自研应用实现分类过滤与快速访问。该方法虽以塞尔维亚语为应用场景，但概念上具备语言无关性，尤其适合低资源、形态丰富的语言。系统性能评估显示，平均处理时间为0.041秒/封邮件，加权F1得分为0.96。目前系统已在公司生产环境运行，显著优化了客户服务流程。
原文摘要 · Abstract (English)
This study introduces a novel natural language processing pipeline that enhances customer service efficiency at Telekom Srbija, a leading Serbian telecommunications company, through automated email topic detection and labeling. Central to the pipeline is BERTopic, a modular framework that allows unsupervised topic modeling. After a series of preprocessing and postprocessing steps, we assign one of 12 topics and several additional labels to incoming emails, allowing customer service to filter and access them through a custom-made application. While applied to Serbian, the methodology is conceptually language-agnostic and can be readily adapted to other languages, particularly those that are low-resourced and morphologically rich. The system performance was evaluated by assessing the speed and correctness of the automatically assigned topics, with a weighted average processing time of 0.041 seconds per email and a weighted average F1 score of 0.96. The system now operates in the company's production environment, streamlining customer service operations through automated email classification.
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